code: def _evaluate(chunk):
      letters = iter(
          letter for letter in ascii_letters if letter not in "ni"
      )
  
      input_types = [i.dtype.name for i in chunk.op.inputs]
      input_names = {i: next(letters) for i in chunk.op.inputs}
      input_arguments = ", ".join(
          [
              f"{tp} {input_names[i]}"
              for i, tp in zip(chunk.op.inputs, input_types)
          ]
      )
      output_type = chunk.op.dtype.name
      output_name = next(letters)
      output_argument = f"{output_type} {output_name}"
      body = dict(input_names)
  
      for node in chunk.composed:
          op_cls = type(node.op)
          if op_cls in CP_BINOP_TO_STRING:
              input_bodies = [
                  body.get(i, repr(i))
                  for i in (node.op.lhs, node.op.rhs)
              ]
              body[node] = f" {CP_BINOP_TO_STRING[op_cls]} ".join(
                  input_bodies
              )
          elif op_cls in CP_UNARYOP_TO_STRING:
              input_data = body[node.op.inputs[0]]
              body[
                  node
              ] = f"{CP_UNARYOP_TO_STRING[op_cls]}({input_data})"
          else:
              raise NotImplementedError
  
      body = f"{output_name} = {body[chunk.composed[-1]]}"
      key = tokenize(input_arguments, output_argument, body)
      return (
          input_arguments,
          output_argument,
          body,
          f"{type(chunk.op).__name__.lower()}_{key}",
      )
  
file_path: mars/mars/tensor/fuse/cupy.py
imports: from string import ascii_letters
  from ...utils import tokenize
  from ..operands import TensorFuse
  from ..None import arithmetic
  from .core import TensorFuseChunkMixin
label_desc: Use image node_id 1 for calling a global function with example usage: _evaluate(chunk) and returns: input_arguments,  output_argument,  body,  type, str, key